Our Technology

A Trust Score on Information

Factmata has developed patent-pending technology to deal with hate speech, propaganda, fake
news and clickbait. Our goal is to be able to provide a real time quality and credibility
score to any piece of content on the web.

Current Capabilities

Hate speech and abusive content

This includes the existence of sexist, racist or ethnic statements that use slur, attack
or
criticize a minority (without a well founded argument), seek to distort views on a
minority with unfounded claims, negatively stereotype a minority, or defend xenophobia
or sexism.

Propaganda and extremely politically biased content

Hyperpartisan news can be understood as extremely one-sided, extremely biased news
articles. These articles provide an unbalanced and provocative point of view in
describing events, and often contain strong sentiment in describing political parties or
politicians, with positive/negative association; insulting/aggravating or slanderous
statements towards people or parties; direct calls to action to support a particular
faction or aggressive campaigning, and finally content which tends to cherry pick
evidence to support its own biases and arguments.

Spoof websites and content spread by known fake news networks

We define fake news as those websites or articles propagating untrue information,
knowing they are untrue, to deceive others. This definition is heavily focused on
intent, as other types of websites containing untrue information (e.g. satire, fiction)
are not included in our definition of fake news. This system picks up fake news content
propagated by known fake news sites (and networks of sites), content that links to such
sites, and sites that try to spoof or plagiarise the branding of reputable news
publishers.

Extreme clickbait content

We define extreme clickbait content as currently focused on articles that have headlines
that are clearly and aggressively created to incentivise clicks and shares. Often, this
type of content has headlines that don’t match the nature of the content being talked
about in the article, promoted aggressively for engagement.

We are soon building algorithms to classify the quality and
credibility of any piece of content, both text and videos.

The Factmata approach

Artificial Intelligence

Our algorithms use advanced natural language processing and artificial intelligence to learn
what different types of deceptive content look like from vast, uniquely annotated and
labelled datasets of example material, and then detect them in the wild.

Communities

To help the algorithms continue to do a better job, we also make use of communities and
people who provide their own feedback.

Expert knowledge

Finally, through our work with expert journalists and social science researchers, we have
developed heuristics which allow us to quickly highlight problematic content.